Precision Auto Components
Real-time 3D digital twin monitoring 4 assembly lines with IoT sensor integration.
All case studiesPrecision Auto Components, one of India's mid-sized automotive parts manufacturers, operated 4 high-speed assembly lines producing brake components and transmission parts for major OEMs. Equipment health monitoring relied on scheduled inspections and operator experience — there was no real-time visibility into machine condition, vibration trends, or thermal anomalies. When a machine failed unexpectedly, the entire line stopped, triggering cascading delays across downstream processes.
Cydez built a photorealistic 3D digital twin of the entire factory floor using Three.js and React Three Fiber, rendered in a browser-based dashboard accessible from the plant manager's office and remotely on tablet devices. Over 120 IoT sensors — vibration, temperature, current draw, and acoustic — were connected via MQTT to AWS IoT TwinMaker. Each machine in the 3D model displays real-time health status with colour-coded indicators, and clicking any asset shows historical trend charts from InfluxDB.
Machine learning models trained on 18 months of historical sensor data predict equipment failures up to 48 hours in advance. When a predictive alert fires, the system auto-generates a maintenance work order in Oracle ERP Cloud, assigns it to the nearest available technician, and highlights the affected machine in the 3D twin with a pulsing alert indicator.
- 3D environment modelling of 4 assembly lines at 1:1 scale
- IoT sensor integration — 120+ sensors via MQTT pipeline
- Real-time data visualisation on 3D twin with colour-coded health indicators
- Predictive maintenance ML model training on 18 months of historical data
- Oracle ERP integration for automated work order generation
- Responsive browser-based dashboard with tablet support
An auto parts manufacturer needed real-time visibility into equipment health across 4 assembly lines. Maintenance was reactive — machines failed without warning, causing costly unplanned downtime averaging 12 hours per month per line.
Cydez built a photorealistic 3D digital twin of the factory floor using Three.js, connected to IoT vibration, temperature, and current sensors via MQTT. The twin displays real-time machine status, predicts failures 48 hours in advance using ML models, and auto-generates maintenance work orders in Oracle ERP.
How we delivered this project
Asset Survey
Conducted a 3-week on-site survey of all 4 assembly lines, photographing and measuring 85 machines. Catalogued existing IoT sensors, identified gaps, and specified additional sensor requirements. Mapped Oracle ERP work order workflows with the maintenance team.
3D Production
Created photorealistic 3D models of all machines and the factory floor layout using Blender. Optimised geometry for real-time WebGL rendering — under 2 million polygons for the full factory scene. Textured and lit the environment to match actual factory conditions.
Development & Integration
Built the Three.js-based 3D viewer with React frontend and Node.js backend. Connected 120+ IoT sensors via MQTT to AWS IoT TwinMaker. Developed the InfluxDB time-series pipeline and trained ML models for predictive maintenance on historical sensor data.
Testing & Deployment
Validated predictive model accuracy against 6 months of held-out data — achieved 92% failure prediction rate. Load-tested the real-time dashboard with 120 concurrent sensor feeds. Deployed incrementally line-by-line over 4 weeks with operator training at each stage.
What we built
Photorealistic 3D Factory Twin
Browser-based 3D model of the entire factory floor at 1:1 scale. Navigate, zoom, and click any machine to view real-time sensor data, maintenance history, and health scores.
Real-Time Sensor Overlay
120+ IoT sensors feeding live data to the 3D twin via MQTT. Colour-coded health indicators — green (normal), amber (warning), red (critical) — visible at a glance across the entire floor.
Predictive Maintenance Engine
ML models analysing vibration, temperature, and current patterns to predict failures 48 hours in advance. Auto-generated maintenance work orders in Oracle ERP with priority scoring.
Historical Trend Analytics
InfluxDB-powered time-series charts for every sensor — 90-day rolling views of vibration amplitude, bearing temperature, motor current, and acoustic signatures with anomaly highlighting.
Remote Monitoring Dashboard
Tablet-optimised dashboard for plant managers and maintenance supervisors to monitor all 4 lines remotely. Push notifications for critical alerts with one-tap work order approval.
Measurable outcomes
- 35% reduction in unplanned downtime within 6 months
- Predictive alerts catching 92% of failures before they occur
- Remote monitoring reducing physical inspection rounds by 60%
- ROI achieved within 8 months of deployment
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